Detecting and tracking regional outliers in meteorological data |
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Authors: | Chang-Tien Lu |
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Affiliation: | a Department of Computer Science, Virginia Polytechnic Institute and State University, 7054 Haycock Road, Falls Church, VA 22043, United States b QSS Group, Inc., 4500 Forbes Blvd Lanham, MD 20706, United States c Department of Computer Science and Information Technology, The University of the District of Columbia, 4200 Connecticut Avenue NW, Washington, DC 20008, United States |
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Abstract: | Detecting spatial outliers can help identify significant anomalies in spatial data sequences. In the field of meteorological data processing, spatial outliers are frequently associated with natural disasters such as tornadoes and hurricanes. Previous studies on spatial outliers mainly focused on identifying single location points over a static data frame. In this paper, we propose and implement a systematic methodology to detect and track regional outliers in a sequence of meteorological data frames. First, a wavelet transformation such as the Mexican Hat or Morlet is used to filter noise and enhance the data variation. Second, an image segmentation method, λ-connected segmentation, is employed to identify the outlier regions. Finally, a regression technique is applied to track the center movement of the outlying regions for consecutive frames. In addition, we conducted experimental evaluations using real-world meteorological data and events such as Hurricane Isabel to demonstrate the effectiveness of our proposed approach. |
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Keywords: | Spatial outlier detection Wavelet Image segmentation Data sequence Meteorological data |
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